Bibliographic record
Abstract
Remnant oak savannah and tallgrass prairie ecosystems are among the most unique and threatened vegetation communities in southern Ontario. Fire suppression, invasive species establishment, and anthropocentrism have led to the transition of remnant oak savannahs to closed-canopy forests (Bachner, 2023). Historically, Indigenous peoples have used prescribed burning to manage these communities, limit woody encroachment, and maintain populations of prairie plants. With less than 3% of Ontario's remnant tallgrass prairie and oak savannah remaining, there is an urgent need not only to protect but also to actively manage these ecosystems and their associated biodiversity (Dinh et al., 2015). The City of Toronto has been a leader in this effort, implementing prescribed burns in High Park, which is an Area of Natural and Scientific Interest (ANSI), to restore and sustain these habitats. To evaluate the effectiveness of prescribed burning, 16 fixed-area plots were established within High Park burn sites using the Vegetation Sampling Protocol (VSP). The collected data enabled the establishment of baseline conditions and the assessment of native plant diversity using the Floristic Quality Index (FQI) to compare frequently and infrequently burned sites. This study found that the frequency of prescribed burns positively affected site quality and abundance of oak species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".